Marginalising posterior covariance matrix with application to Bayesian operational modal analysis
Consider making Bayesian inference of vector-valued model parameters {x,y} based on observed data D. When the ‘posterior’ (i.e., given data) probability density function (PDF) of {x,y} has a centralised shape, it can be approximated in the spirit of Laplace integral asymptotics by a Gaussian PDF cen...
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Main Author: | Au, Siu-Kui |
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Other Authors: | School of Civil and Environmental Engineering |
Format: | Article |
Language: | English |
Published: |
2024
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/180891 |
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Institution: | Nanyang Technological University |
Language: | English |
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